Actuator Input Saturation Research Articles

Event-driven prescribed performance distributed formation control for MSVs based on second-order step response with actuator input saturation

This study aims to investigate the problem of event-driven distributed formation control for marine surface vehicles (MSVs) system, including prescribed performance constraint and actuator input saturation. By constructing a novel prescribed performance function based on second-order step response, the intuitive physical significance is realized, and the performance specifications imposed on the output tracking errors are achieved. Subsequently, a double-layer adaptive sliding-mode disturbance observer (ASMDO) is presented to compensate for lumped uncertain terms. Within the controller design stage, an auxiliary dynamic system is built to introduce the auxiliary intermediate control law, thus avoiding the need to directly design anti-saturation generalized control input for the MSVs system. Then, by incorporating external dynamic variable, a dynamic event-triggered mechanism (DETM) is proposed to regulate the transmission between the controller and actuators. Under the DETM, the designed controller only transmits the control force and torque into actuators at the specified triggering moment, and the trigger moment is independent of the internal state of the system. After that, the minimum inter-event time (MIET) of the proposed DETM is proved to be strictly positive, which ensures Zeno-free behavior. Lastly, the efficiency and advantages of the proposed event-driven distributed formation controller are verified through numerical simulations.

Cooperative Operation Control of Virtual Coupling High-Speed Trains With Input Saturation and Full-State Constraints

In this paper, the distributed cooperative control of virtual coupling high-speed trains (HSTs) subject to full-state constraints, actuator limitations, dynamical uncertainties and environmental disturbances is investigated. Targeting at the full-state constraints in cooperative operation of HSTs, a distributed nonlinear state-dependent function (DNSDF) is first proposed to convert the state-constrained problem of the leader-following consensus control to the boundedness problem of DNSDF. Then, the distributed control law of each train is designed by combining the command filtering backstepping method and the adaptive neural network approximation technique. Meanwhile, combined with the DNSDF, a novel auxiliary dynamical system (ADS) is designed to compensate for the adverse effects of actuator input saturation, and thus ensure the closed-loop stability of the HSTs system when the state constraints and the input saturation are considered simultaneously. By utilizing the Lyapunov theory, the convergence of the proposed controller is analyzed. Finally, the feasibility and effectiveness of the proposed control scheme are verified by simulations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work was motivated by the problem of cooperative control for virtual coupling HSTs with different initial states, actuator input saturation, full-state constraints, etc. The proposed approach addresses the train position and speed constraints by applying DNSDF, which can ensure train coordinated operation with relative braking distance and further reduce the tracking interval between trains. Specifically, the upper bound of the train position constraint varies with the real-time position of the preceding train and the relative speed of the adjacent trains, rather than a constant value. More importantly, an ADS is designed based on the DNSDF, which guarantees the stability of the controller when the input saturation and state constraints exist simultaneously, and avoids the chattering phenomenon of the train control input. The simulation results show that the trains can operate cooperatively at any initial speed within the speed limitations. In future, we will focus on the cooperative control of HSTs with communication delay, and the energy saving optimization cooperative control of HSTs.

Adaptive finite-time fault-tolerant control for the full-state-constrained robotic manipulator with novel given performance

Most robot manipulators usually operate under different nonlinearities, such as state constraints, time delays, and actuator input saturation faults. This paper proposes an adaptive finite-time fault-tolerant controller for the full-state-constrained robotic manipulator with novel prescribed performance and time-varying delays. Firstly, a novel prescribed performance function consisting of hyperbolic tangent and hyperbolic cotangent functions is constructed to the full-state performance constraints of the robotic manipulator. Such a design can circumvent the demands for accurate initial conditions of the state variables and guarantee the convergence of the state errors in a prescribed time. Then, based on the finite-time principle, a finite-time command filter and a compensation mechanism with fractional-power terms are used to bypass the “explosion of complexity” and further compensate for the filter errors within a finite time. Moreover, a proper Lyapunov–Krasovskii functional is devised within the controller design to compensate for the time-varying delays. And the radial basis function neural networks are used to estimate the other nonlinearities including actuator saturation faults, and external perturbations. Finally, simulation results exhibit that the controller designed in this paper has a faster convergence speed and minor state errors.

Boundary Vibration Constraint Control Design of Motor-Driven Industrial Moving Belt Systems Subject to Actuator Input Saturation

This paper investigates the adaptive controller design problem for a class of motor-driven industrial belt systems with boundary vibration constraints and actuator input saturation. The operating equations of the considered system are obtained based on the Eulerian description and Hamilton’s principle, and an auxiliary system is desiigned to compensate for the input saturation effect of the actuator. In a practical engineering context, the system is not only affected by the time-varying boundary disturbances but also the key parameters of the whole closed-loop system are unknown. For this reason, a disturbance observer and the adaptive laws are designed to estimate the disturbances suffered by the system and to compensate for the parameter uncertainty of the system, respectively. With the application of our designed control strategy, the boundary vibration of the system are shown to satisfy the constraint, i.e., the safe operation of the motor-driven industrial belt system is ensured. Finally, the stability and effectiveness of the designed control scheme are demonstrated by examples of numerical simulations.

Finite-Time Neural Network Fault-Tolerant Control for Robotic Manipulators under Multiple Constraints

In this study, a backstepping-based fault-tolerant controller for a robotic manipulator system with input and output constraints was developed. First, a barrier Lyapunov function was adopted to ensure that the system output satisfied time-varying constraints. Subsequently, the actuator input saturation and asymmetric dead-zone characteristics were also considered, and the actuator characteristics were described using a continuous function. The impacts of actuator failures and unknown dynamical parameters of the system were eliminated by employing Gaussian radial basis function neural networks. The external disturbances were compensated for, using a disturbance observer. Meanwhile, a finite-time dynamic surface technique was adopted to accelerate the convergence of the system errors. Finally, simulation of a 2-degrees-of-freedom robotic manipulator system showed the effectiveness of the proposed controller.

Event-triggered formation control of AUVs with fixed-time RBF disturbance observer

Aiming at the problem of model parameter uncertainty, unknown current disturbance and actuator input saturation of multi-AUVs formation control, an event-triggered formation strategy based on fixed-time Radial Basis Function(RBF) neural network adaptive disturbance observer is proposed, which can ensure formation control converge in fixed time. First of all, fixed time RBF disturbance observer (FRBFDO) is put forward to estimate lumped disturbance accurately. Based on the FRBFDO, with the combination of command filter and the back-stepping method, which is used to eliminate repeated derivation calculation explosion problem; Secondly, in order to save the energy consumption of network transmission resources, the event trigger mechanism is introduced into the multi-AUVs formation control. The fixed-time distributed formation controller is designed to realize the fixed-time stability of formation system, and the system convergence time is independent of the initial state. Finally, the effectiveness and rationality of the proposed algorithm are proved by the multi-AUVs formation simulation experiment.

Pitch autopilot design for an autonomous aerial vehicle in the presence of amplitude and rate saturation

In this article, a new composite anti-windup (AW) compensator scheme is proposed to tackle the rate and amplitude saturations in an autonomous aerial vehicle (AAV). The actuator input saturation is a practical and critical challenge in the controller design of autonomous vehicles. Nowadays, modern anti-windup compensators are presenting practical remedies to cope with input amplitude and rate saturation. Meanwhile, Riccati-based compensators can be a suitable choice due to their high performance and simplicity. Therefore, this article proposes an integrated approach by combining the anti-windup amplitude and rate methods to deal with the aforementioned problems. First, the basic controller function on the linear model is examined. It is shown that this controller behaves well so long as there is no saturation. However, when saturation occurs, its performance is decreased and even becomes unstable. By modifying the controller via the proposed method, the controller efficiency is increased significantly. The proposed method has been implemented on an autonomous aerial vehicle with 6-degrees of freedom (6DOF). Through various simulation scenarios, it is shown that the proposed method can show proper behavior in the case of a nonlinear and close-to-reality model of the system. In addition, the proposed controller efficacy has been investigated in a comprehensive Monte Carlo simulation and it is shown that the proposed method keeps its efficiency against the practical uncertainties of the system behavior.

Adaptive Reinforcement Learning Neural Network Control for Uncertain Nonlinear System With Input Saturation.

In this paper, an adaptive neural network (NN) control problem is investigated for discrete-time nonlinear systems with input saturation. Radial-basis-function (RBF) NNs, including critic NNs and action NNs, are employed to approximate the utility functions and system uncertainties, respectively. In the previous works, a gradient descent scheme is applied to update weight vectors, which may lead to local optimal problem. To circumvent this problem, a multigradient recursive (MGR) reinforcement learning scheme is proposed, which utilizes both the current gradient and the past gradients. As a consequence, the MGR scheme not only eliminates the local optimal problem but also guarantees faster convergence rate than the gradient descent scheme. Moreover, the constraint of actuator input saturation is considered. The closed-loop system stability is developed by using the Lyapunov stability theory, and it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed approach is further validated via some simulation results.

Tracking control of a large range 3D printed compliant nano-manipulator with enhanced anti-windup compensation

Abstract This paper studies tracking of a large stroke 3D printed XY compliant nano-manipulator with spatial constraints. To asymptotically track periodic references, an internal model principle-based control is utilized as the baseline tracking controller. To avoid the actuator input saturation in relatively large stroke, an enhanced anti-windup scheme with an integrated compensation strategy is developed, where the anticipatory anti-windup compensator and the conventional one are adopted in different operation modes. The design of the modified anti-windup compensator is decoupled with the internal model principle-based controller to facilitate the integrated control design. The synthesis of linear matrix inequality (LMI) is also provided to obtain the parameters of the dynamic anti-windup compensator. The proposed control strategy is experimentally validated, where the tracking error is 62.54 nm (RMS) in absence of saturation and significant error reduction in the presence of saturation is demonstrated compared with the conventional anti-windup compensation.

Adaptive Robust Control for Mirror-Stabilized Platform With Input Saturation

The line-of-sight (LOS) kinematics and dynamics of a mirror-stabilized platform are derived using the virtual mass stabilization method. Accounting for the coupled and nonlinear kinematics and dynamics, the uncertainty of external disturbances, and the actuator input saturation in the mirror-stabilized platform, a modified adaptive robust control (ARC) scheme is proposed based on the command filtered method and the extended state observer (ESO). The command-filtered approach is used to ensure the stability and tracking performance of the adaptive control system under the input saturation. In the proposed scheme, the ESO is designed to observe the modeling error and unknown external disturbances. The stability of the control system is proved using the Lyapunov method. Simulation results and experimental results proved that the proposed control scheme can effectively reduce the occurrence of input saturation, attenuate the effect of unknown disturbances, and improve the position tracking accuracy.

Disturbance observer based finite-time attitude control for rigid spacecraft under input saturation

A novel finite-time controller integrated with disturbance observer is investigated for a rigid spacecraft in the presence of disturbance, actuator saturation and misalignment. As a stepping-stone, a second-order disturbance observer is designed firstly such that the reconstruction of lumped disturbances is accomplished in finite time with zero error. Then, with the reconstructed information, a finite-time controller is synthesized even under actuator input saturation and misalignment, and the closed-loop system/state is proved to be finite-time stable and converges to the specified time-varying sliding mode surface. Moreover, the input saturation constraint is overcome via introducing an auxiliary variable to compensate for the overshooting. Numerical simulation results for the in-orbit rigid spacecraft show good performances, which validate the effectiveness and feasibility of the proposed schemes.

Observer-Based Fault Diagnosis Incorporating Online Control Allocation for Spacecraft Attitude Stabilization under Actuator Failures

This paper proposes a novel observer-based fault diagnosis method, incorporating an online control allocation scheme, for an orbiting spacecraft in the presence of actuator failures/faults, unexpected disturbances and input saturation. The proposed scheme solves a difficult problem in spacecraft fault tolerant control design by compensating for the loss in effectiveness and time-varying faults using redundant actuators, so that the overall system is stable, despite external disturbances and input saturation. This is accomplished by developing an observer-based fault diagnosis mechanism to reconstruct or estimate the actuator faults/failures. An online control allocation scheme is then used to redistribute the control signals to the healthy actuators in the case of faults/failures, without reconfiguring the controller, so that the control signal distribution is based on the reconstructed actuator effectiveness level. Simulation results using a rigid spacecraft model show good performance in the presence of faults, including total actuator failure scenarios, external disturbances and actuator input saturation, which validates the effectiveness and feasibility of the proposed scheme.

Intelligent proportional-derivative control for flexible spacecraft attitude stabilization with unknown input saturation

A novel attitude stabilization control scheme is presented for flexible spacecraft, based on the semi-globally input-to-state stable (ISS) concept. The modified Rodrigues parameters described attitude system is considered in the presence of unknown parameter uncertainties, external disturbances and even control input saturation. A simple and nonlinear proportional-plus-derivative (PD) controller is first developed through a special Lyapunov function construction. Sufficient condition under which the proposed nonlinear PD-type control law can render the system semi-globally input-to-state stable is provided such that the closed-loop system is robust with respect to any disturbance and uncertain inertia parameters, if the control gains are designed appropriately. The derived controller is then extended by adding a feed-forward compensator to reduce the effect of the unknown control input saturation on the system, while the compensator is derived by using radial basis function neural networks to approximate the unknown nonlinearity. It is shown that the extended control law can guarantee that the resulting closed-loop attitude system is ISS despite of actuator input saturation. The result is constructive in the sense that given any restriction on the initial conditions, it is possible to design large enough control gains that can render the system semi-globally ISS within the given restriction. Simulation studies have also been conducted to confirm and verify those highly desirable features of the proposed attitude control in comparison with the conventional control schemes.